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@ -427,7 +427,7 @@ Read the paper [here](https://arxiv.org/abs/1902.06714).
* [gobrain](https://github.com/goml/gobrain) - Neural Networks written in Go.
* [go-featureprocessing](https://github.com/nikolaydubina/go-featureprocessing) - Fast and convenient feature processing for low latency machine learning in Go.
* [go-mxnet-predictor](https://github.com/songtianyi/go-mxnet-predictor) - Go binding for MXNet c_predict_api to do inference with a pre-trained model.
* [go-ml-benchmarks](https://github.com/nikolaydubina/go-ml-benchmarks) — benchmarks of machine learning inference for Go
* [go-ml-benchmarks](https://github.com/nikolaydubina/go-ml-benchmarks) — benchmarks of machine learning inference for Go.
* [go-ml-transpiler](https://github.com/znly/go-ml-transpiler) - An open source Go transpiler for machine learning models.
* [golearn](https://github.com/sjwhitworth/golearn) - Machine learning for Go.
* [goml](https://github.com/cdipaolo/goml) - Machine learning library written in pure Go.
@ -522,16 +522,16 @@ Read the paper [here](https://arxiv.org/abs/1902.06714).
* [AMIDST Toolbox](http://www.amidsttoolbox.com/) - A Java Toolbox for Scalable Probabilistic Machine Learning.
* [Datumbox](https://github.com/datumbox/datumbox-framework) - Machine Learning framework for rapid development of Machine Learning and Statistical applications.
* [ELKI](https://elki-project.github.io/) - Java toolkit for data mining. (unsupervised: clustering, outlier detection etc.)
* [Encog](https://github.com/encog/encog-java-core) - An advanced neural network and machine learning framework. Encog contains classes to create a wide variety of networks, as well as support classes to normalize and process data for these neural networks. Encog trains using multithreaded resilient propagation. Encog can also make use of a GPU to further speed processing time. A GUI based workbench is also provided to help model and train neural networks.
* [Encog](https://github.com/encog/encog-java-core) - An advanced neural network and machine learning framework. Encog contains classes to create a wide variety of networks, as well as support classes to normalize and process data for these neural networks. Encog trainings using multithreaded resilient propagation. Encog can also make use of a GPU to further speed processing time. A GUI based workbench is also provided to help model and train neural networks.
* [FlinkML in Apache Flink](https://ci.apache.org/projects/flink/flink-docs-master/dev/libs/ml/index.html) - Distributed machine learning library in Flink.
* [H2O](https://github.com/h2oai/h2o-3) - ML engine that supports distributed learning on Hadoop, Spark or your laptop via APIs in R, Python, Scala, REST/JSON.
* [htm.java](https://github.com/numenta/htm.java) - General Machine Learning library using Numenta’s Cortical Learning Algorithm.
* [liblinear-java](https://github.com/bwaldvogel/liblinear-java) - Java version of liblinear.
* [Mahout](https://github.com/apache/mahout) - Distributed machine learning.
* [Meka](http://meka.sourceforge.net/) - An open source implementation of methods for multi-label classification and evaluation (extension to Weka).
* [MLlib in Apache Spark](https://spark.apache.org/docs/latest/mllib-guide.html) - Distributed machine learning library in Spark
* [MLlib in Apache Spark](https://spark.apache.org/docs/latest/mllib-guide.html) - Distributed machine learning library in Spark.
* [Hydrosphere Mist](https://github.com/Hydrospheredata/mist) - a service for deployment Apache Spark MLLib machine learning models as realtime, batch or reactive web services.
* [Neuroph](http://neuroph.sourceforge.net/) - Neuroph is lightweight Java neural network framework
* [Neuroph](http://neuroph.sourceforge.net/) - Neuroph is lightweight Java neural network framework.
* [ORYX](https://github.com/oryxproject/oryx) - Lambda Architecture Framework using Apache Spark and Apache Kafka with a specialization for real-time large-scale machine learning.
* [Samoa](https://samoa.incubator.apache.org/) SAMOA is a framework that includes distributed machine learning for data streams with an interface to plug-in different stream processing platforms.
* [RankLib](https://sourceforge.net/p/lemur/wiki/RankLib/) - RankLib is a library of learning to rank algorithms. **[Deprecated]**
@ -564,7 +564,7 @@ Read the paper [here](https://arxiv.org/abs/1902.06714).
#### Deep Learning
* [Deeplearning4j](https://github.com/deeplearning4j/deeplearning4j) - Scalable deep learning for industry with parallel GPUs.
* [Keras Beginner Tutorial](https://victorzhou.com/blog/keras-neural-network-tutorial/) - Friendly guide on using Keras to implement a simple Neural Network in Python
* [Keras Beginner Tutorial](https://victorzhou.com/blog/keras-neural-network-tutorial/) - Friendly guide on using Keras to implement a simple Neural Network in Python.
* [deepjavalibrary/djl](https://github.com/deepjavalibrary/djl) - Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning, designed to be easy to get started with and simple to use for Java developers.
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@ -578,7 +578,7 @@ Read the paper [here](https://arxiv.org/abs/1902.06714).
* [Knwl.js](https://github.com/loadfive/Knwl.js) - A Natural Language Processor in JS.
* [Retext](https://github.com/retextjs/retext) - Extensible system for analyzing and manipulating natural language.
* [NLP Compromise](https://github.com/spencermountain/compromise) - Natural Language processing in the browser.
* [nlp.js](https://github.com/axa-group/nlp.js) - An NLP library built in node over Natural, with entity extraction, sentiment analysis, automatic language identify, and so more
* [nlp.js](https://github.com/axa-group/nlp.js) - An NLP library built in node over Natural, with entity extraction, sentiment analysis, automatic language identify, and so more.
@ -643,7 +643,7 @@ Read the paper [here](https://arxiv.org/abs/1902.06714).
* [JSMLT](https://github.com/jsmlt/jsmlt) - Machine learning toolkit with classification and clustering for Node.js; supports visualization (see [visualml.io](https://visualml.io)).
* [xgboost-node](https://github.com/nuanio/xgboost-node) - Run XGBoost model and make predictions in Node.js.
* [Netron](https://github.com/lutzroeder/netron) - Visualizer for machine learning models.
* [tensor-js](https://github.com/Hoff97/tensorjs) - A deep learning library for the browser, accelerated by WebGL and WebAssembly
* [tensor-js](https://github.com/Hoff97/tensorjs) - A deep learning library for the browser, accelerated by WebGL and WebAssembly.
* [WebDNN](https://github.com/mil-tokyo/webdnn) - Fast Deep Neural Network Javascript Framework. WebDNN uses next generation JavaScript API, WebGPU for GPU execution, and WebAssembly for CPU execution.
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@ -706,7 +706,7 @@ Read the paper [here](https://arxiv.org/abs/1902.06714).
* [ScikitLearn](https://github.com/cstjean/ScikitLearn.jl) - Julia implementation of the scikit-learn API.
* [Knet](https://github.com/denizyuret/Knet.jl) - Koç University Deep Learning Framework.
* [Flux](https://fluxml.ai/) - Relax! Flux is the ML library that doesn't make you tensor
* [MLJ](https://github.com/alan-turing-institute/MLJ.jl) - A Julia machine learning framework
* [MLJ](https://github.com/alan-turing-institute/MLJ.jl) - A Julia machine learning framework.
<a name="julia-natural-language-processing"></a>
#### Natural Language Processing